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Strategies for exome and genome sequence data analysis in disease-gene discovery projects

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50496

Robinson,  P. N.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50437

Mundlos,  S.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

Robinson, P. N., Krawitz, P., & Mundlos, S. (2011). Strategies for exome and genome sequence data analysis in disease-gene discovery projects. Clin Genet, 80(2), 127-32. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21615730 http://onlinelibrary.wiley.com/doi/10.1111/j.1399-0004.2011.01713.x/abstract.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-784A-C
Zusammenfassung
In whole-exome sequencing (WES), target capture methods are used to enrich the sequences of the coding regions of genes from fragmented total genomic DNA, followed by massively parallel, 'next-generation' sequencing of the captured fragments. Since its introduction in 2009, WES has been successfully used in several disease-gene discovery projects, but the analysis of whole-exome sequence data can be challenging. In this overview, we present a summary of the main computational strategies that have been applied to identify novel disease genes in whole-exome data, including intersect filters, the search for de novo mutations, and the application of linkage mapping or inference of identity-by-descent (IBD) in family studies.